Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction

<p>Abstract</p> <p>Background</p> <p>Reliable predictions of Cytotoxic T lymphocyte (CTL) epitopes are essential for rational vaccine design. Most importantly, they can minimize the experimental effort needed to identify epitopes. NetCTL is a web-based tool designed for predicting human CTL epitopes in any given protein. It does so by integrating predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I affinity. At least four other methods have been developed recently that likewise attempt to predict CTL epitopes: EpiJen, MAPPP, MHC-pathway, and WAPP. In order to compare the performance of prediction methods, objective benchmarks and standardized performance measures are needed. Here, we develop such large-scale benchmark and corresponding performance measures and report the performance of an updated version 1.2 of NetCTL in comparison with the four other methods.</p> <p>Results</p> <p>We define a number of performance measures that can handle the different types of output data from the five methods. We use two evaluation datasets consisting of known HIV CTL epitopes and their source proteins. The source proteins are split into all possible 9 mers and except for annotated epitopes; all other 9 mers are considered non-epitopes. In the RANK measure, we compare two methods at a time and count how often each of the methods rank the epitope highest. In another measure, we find the specificity of the methods at three predefined sensitivity values. Lastly, for each method, we calculate the percentage of known epitopes that rank within the 5% peptides with the highest predicted score.</p> <p>Conclusion</p> <p>NetCTL-1.2 is demonstrated to have a higher predictive performance than EpiJen, MAPPP, MHC-pathway, and WAPP on all performance measures. The higher performance of NetCTL-1.2 as compared to EpiJen and MHC-pathway is, however, not statistically significant on all measures. In the large-scale benchmark calculation consisting of 216 known HIV epitopes covering all 12 recognized HLA supertypes, the NetCTL-1.2 method was shown to have a sensitivity among the 5% top-scoring peptides above 0.72. On this dataset, the best of the other methods achieved a sensitivity of 0.64. The NetCTL-1.2 method is available at <url>http://www.cbs.dtu.dk/services/NetCTL</url>.</p> <p>All used datasets are available at <url>http://www.cbs.dtu.dk/suppl/immunology/CTL-1.2.php</url>.</p

In-silico design of an Epitope-based peptide vaccine: A Computational Biology Approach

Lymphocytic choriomeningitis, is a rodent-borne viral infectious disease caused by Lymphocytic choriomeningitis virus (LCMV), a member of the family Arenaviridae, that was initially isolated in 1933. Acquired postnatal infection ranges from asymptomatic to a brief, nonspecific flu-like illness to critical self-resolving neurological disease, predominantly consisting of aseptic meningitis or meningoencephalitis. This study was undertaken to design an epitope-based peptide vaccine against Lymphocytic choriomeningitis virus using a computational biology approach. Twenty four sequences of LCMV were retrieved from UniProt database and analyzed with various in silico tools. VaxiJen was used to identify immunogenic peptides and T-cell epitopes were analysed using NetCTL server to identify T-cell epitopes. Out of 15 immunogenic peptides analysed using NetCTL server, a conservancy of 64.28% amongst all epitopes was observed. The peptide sequence VVQNLDQLY, a non-allergen, was found to be a potent T-cell epitope that interacted with 28 human leukocyte antigens (HLAs) and its interaction with HLA-A*02:06 was studied using protein-protein docking analysis. The HLA allele and the epitope VVQNLDQLY were found to effectively interact with each other and this epitope may be used as a vaccine against LCMV. Thus immunoinformatics based approaches can be used to predict vaccine candidates against pathogens in a timely manner and usher us into an era of T-cell based novel vaccinomics approach

Immune epitope database analysis resource (IEDB-AR)

We present a new release of the immune epitope database analysis resource (IEDB-AR, http://tools.immuneepitope.org), a repository of web-based tools for the prediction and analysis of immune epitopes. New functionalities have been added to most of the previously implemented tools, and a total of eight new tools were added, including two B-cell epitope prediction tools, four T-cell epitope prediction tools and two analysis tools

In Silico Vaccine Design for Multidrug-Resistant Staphylococcus Aureus Clumping Factor A (ClfA)

Staphylococcus aureus a facultative anaerobic multidrug-resistant bacterium can cause a range of illnesses, from minor skin infections, such as pimples, boils, impetigo, folliculitis, cellulitis, carbuncles, scalded skin syndrome, and abscesses and life-threatening diseases such as meningitis, pneumonia, bacteremia, sepsis, osteomyelitis, endocarditis and toxic shock syndrome. Pathogenic strains often promote infections by producing virulence factors and the expression of cell-surface proteins that bind and inactivate antibodies. The emergence of antibiotic-resistant strains of S. aureus such as methicillin resistant S. aureus (MRSA) is a worldwide problem in clinical medicine. In spite of immense research and development, not much progress has been made with regard to an epitope based vaccine and till date there is no approved vaccine for S. aureus. This study aims to analyze and predict the possibility of designing a vaccine that could make humans immune to S. aureus. The surface protein ClfA is highly antigenic among the virulence factors of S. aureus which act as an adhesin often essential for infection was collected from a protein database and in silico tools were used to predict the T-cell epitopes by NetCTL 1.2 and B-cell epitopes by Bepipred from IEDB (Immune Epitope Database). Further, MHC Class I and Class II binding peptides were predicted using TepiTool from IEDB analysis resource. The peptide KPNTDSNAL was found as the most potential B-cell and T-cell epitope. The epitope was further tested for binding against the HLA molecule by computational docking techniques to verify the HLA and epitope interaction. However, the in silico designed epitope-based peptide vaccine against S. aureus need to be validated by in vitro and in vivo experiments

ViPR: an open bioinformatics database and analysis resource for virology research

The Virus Pathogen Database and Analysis Resource (ViPR, www.ViPRbrc.org) is an integrated repository of data and analysis tools for multiple virus families, supported by the National Institute of Allergy and Infectious Diseases (NIAID) Bioinformatics Resource Centers (BRC) program. ViPR contains information for human pathogenic viruses belonging to the Arenaviridae, Bunyaviridae, Caliciviridae, Coronaviridae, Flaviviridae, Filoviridae, Hepeviridae, Herpesviridae, Paramyxoviridae, Picornaviridae, Poxviridae, Reoviridae, Rhabdoviridae and Togaviridae families, with plans to support additional virus families in the future. ViPR captures various types of information, including sequence records, gene and protein annotations, 3D protein structures, immune epitope locations, clinical and surveillance metadata and novel data derived from comparative genomics analysis. Analytical and visualization tools for metadata-driven statistical sequence analysis, multiple sequence alignment, phylogenetic tree construction, BLAST comparison and sequence variation determination are also provided. Data filtering and analysis workflows can be combined and the results saved in personal ‘Workbenches’ for future use. ViPR tools and data are available without charge as a service to the virology research community to help facilitate the development of diagnostics, prophylactics and therapeutics for priority pathogens and other viruses

Predicting Multi-Epitope Peptide Cancer Vaccine from Novel TAA Topo48

Cancer is one of the most lethal diseases. Recently, cancer immunotherapy has a tremendous achievement in cancer treatment. A certain number of cancer based epitope vaccines with different moiety have been discovered. In japan, several clinical tests of cancer based epitope vaccine derived from tumor associated antigens (TAAs) are now ongoing or have recently been completed. a novel of TAAs potentially as cancer vaccine have been retrieved from a fragment weighed 48kDa derived from human DNA-topoisomerase 1 (TOP1) called Topo48. Therefore, it is still critical to discover a derived Topo48 epitope based cancer vaccine. Immuno-informatics considered as a methods noted to have better accuracy to design promising vaccine candidates. Here, continuous and discontinuous B-cell epitopes following with CTL epitopes and their docking interaction to major histocompatibility complex (MHC) class I Human Leukocyte Antigens (HLA)- A0201 were predicted. Kolaskar-Tongaonkar’s, Emini’s, Karpus-Schulz’s, and Parker’s methods were used to predict continuous B-cell epitopes while ElliPro was used for prediction of discontinued B-cell epitopes. Those considered methods marked to have better accuracy to design promising vaccine candidates.&nbsp; Similarly, CTL epitopes was also predicted by using NetCTL server and the best candidates were further investigated their binding affinity by mean of PEP-FOLD3, PatchDock rigid-body docking server, and FireDock server. Total 27 continuous epitopes and 7 discontinuous B-cell epitopes were predicted. In the other hand, 9 peptides were predicted as CTL epitopes. Whereas, three predicted CTL epitope in range 263MLDHEYTTK27, 755AIDMADEDY763, 715ALGTSKLNY724) exhibited good interactions to HLA-A0201. Moreover, we also found residues His266, Thr270, Ala755, Tyr723, Thr718, Ser719, Lys720 from Topo48 and residues Thr163, Asp757, His70, Glu63 from HLA- A0201 were indicated to be antigenic. Ultimately, our proposed continuous/discontinuous B-cell epitopes, and also CTL epitopes can be potential vaccines for cancer immunotherapy

Identifying Schistosoma japonicum Excretory/Secretory Proteins and Their Interactions with Host Immune System

Schistosoma japonicum is a major infectious agent of schistosomiasis. It has been reported that large number of proteins excreted and secreted by S. japonicum during its life cycle are important for its infection and survival in definitive hosts. These proteins can be used as ideal candidates for vaccines or drug targets. In this work, we analyzed the protein sequences of S. japonicum and found that compared with other proteins in S. japonicum, excretory/secretory (ES) proteins are generally longer, more likely to be stable and enzyme, more likely to contain immune-related binding peptides and more likely to be involved in regulation and metabolism processes. Based on the sequence difference between ES and non-ES proteins, we trained a support vector machine (SVM) with much higher accuracy than existing approaches. Using this SVM, we identified 191 new ES proteins in S. japonicum, and further predicted 7 potential interactions between these ES proteins and human immune proteins. Our results are useful to understand the pathogenesis of schistosomiasis and can serve as a new resource for vaccine or drug targets discovery for anti-schistosome

T-Cell Epitope Prediction: Rescaling Can Mask Biological Variation between MHC Molecules

Theoretical methods for predicting CD8+ T-cell epitopes are an important tool in vaccine design and for enhancing our understanding of the cellular immune system. The most popular methods currently available produce binding affinity predictions across a range of MHC molecules. In comparing results between these MHC molecules, it is common practice to apply a normalization procedure known as rescaling, to correct for possible discrepancies between the allelic predictors. Using two of the most popular prediction software packages, NetCTL and NetMHC, we tested the hypothesis that rescaling removes genuine biological variation from the predicted affinities when comparing predictions across a number of MHC molecules. We found that removing the condition of rescaling improved the prediction software's performance both qualitatively, in terms of ranking epitopes, and quantitatively, in the accuracy of their binding affinity predictions. We suggest that there is biologically significant variation among class 1 MHC molecules and find that retention of this variation leads to significantly more accurate epitope prediction

In silico Vaccine Design against Dengue Virus Type 2 Envelope Glycoprotein

Dengue fever is caused by the mosquito-borne virus termed (DENV). However, DENV-2 has been identified as the most prevalent amongst the Indonesian pediatric urban population, in contrast with the other four serotypes. Therefore, it is important to reduce severe infection risk by adopting preventive measures, including through vaccine development. The aim of this study, therefore is to use various in silico tools in the design of epitope-based peptide vaccines (T-cell and B-cell types), based on the DENV-2 envelope glycoprotein sequences available. Therefore, in silico methods were adopted in the analysis of the retrieved protein sequences. This technique was required to determine the most immunogenic protein, and is achieved through conservancy analysis, epitope identification, molecular simulation, and allergenicity assessment. Furthermore, B4XPM1, and KAWLVHRQW were identified from positions 204-212, while the 77 to 85 peptide region was considered the most potent T-cell and B-cell epitopes. The interaction between KAWLVHRQW and HLA-C*12:03 occurs with maximum population coverage, alongside high conservancy (96.98%) and binding affinity. These results indicated a potential for the designed epitopes to demonstrate high immunity against DENV-2